![]() ![]() Flume AgentĪ Flume agent is a long-running Java process that runs on Source – Channel – Sink Combination. Flume supports various sinks like HDFS Sink, Hive Sink, Thrift Sink, Avro Sink.įig 1.1 Basic Flume Architecture 4. There is no rule such that the sink should deliver events to Store, instead, we can configure it in such a way that a sink can deliver events to another agent. Flume sink consumes events from Channel and stores them to Destination stores like HDFS. Flume SinkĪ Flume Sink is present on Data repositories like HDFS, HBase. In Memory, channel events are stored in memory, so it is no durable but very fast in nature. File channel is durable in nature that means once the data is written to channel it will be not lost, although if the agent restarts. Flume Channels are transactional in nature.įlume provides support for the File channel and Memory channel. Channel acts as an intermediate bridge between Source and Sink. Flume ChannelĪn Intermediate Store that buffers the Events sent by Flume Source until they are consumed by Sink is called Flume Channel. Flume supports various types of sources like Avro Flume Source-connects on Avro port and receives events from Avro external client, Thrift Flume Source- connects on Thrift port and receives events from external Thrift client streams, Spooling Directory Source, and Kafka Flume Source. Source collects data from the generator and transfers that data to Flume Channel in the form of Flume Events. Flume SourceĪ Flume Source is present on Data generators like Face book or Twitter. Let us take a brief look of each Flume component 1. In general Apache Flume architecture is composed of the following components: Flume is capable to write to other Frameworks like Hbase or Solr. Although Flume is used to transmit to Hadoop, there is no rigid rule that the destination must be Hadoop. Flume collects those files as events and ingests them to Hadoop. ![]() Kafka will treat each topic partition as an ordered set of messages.įlume can take in streaming data from the multiple sources for storage and analysis which use in Hadoop.Hadoop, Data Science, Statistics & othersĬonsider a scenario where the number of web servers generates log files and these log files need to transmit to the Hadoop file system. Kafka runs as a cluster which handles the incoming high volume data streams in the real time.įlume is a tool to collect log data from distributed web servers. You will lose events in the channel in case of flume-agent failure. It supports automatic recovery if resilient to node failure. It is not scalable in comparison with Kafka.Īn fault-tolerant, efficient and scalable messaging system. It is efficiently collecting, aggregating and moving large amounts of log data from many different sources to a centralized data store. ![]() It is optimized for ingesting and processing streaming data in real-time. It has a flexible design.īelow is a table of differences between Apache Kafka and Apache Flume: Apache KafkaĪpache Kafka is a distributed data system.Īpache Flume is a available, reliable, and distributed system. It has its own query processing engine which makes it to transform each new batch of data before it is moved to the intended sink. It has a flexible and simple architecture based on streaming data flows. It also guarantees zero percent data loss.Īpache Kafka generally used for real-time analytics, ingestion data into the Hadoop and to spark, error recovery, website activity tracking.įlume: Apache Flume is a reliable, distributed, and available software for efficiently aggregating, collecting, and moving large amounts of log data. ![]() It is very fast and performs 2 million writes per second. Kafka generally used TCP based protocol which optimized for efficiency. Apache Kafka aims to provide a high throughput, unified, low-latency platform for handling the real-time data feeds. It is made by LinkedIn which is given to the Apache Software Foundation.
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